Integrated Planning Framework For Pumped Hydro Energy Storage (PHES) Systems
Bibliographic record
Abstract
The electric power industry worldwide has been focusing towards increasing utilization of renewable energy resources such as wind and solar to build and maintain clean, reliable and affordable electricity systems. Although these resources are environmentally clean, their uncertain and intermittent nature is a significant issue. Similarly, other energy generators such as nuclear and gas have serious environmental issues. These issues can be resolved with effective management of supply and demand using appropriate energy storage. Although various energy storage options are available, PHES is globally proven technology at grid level. Additionally, gravity power module (GPM) is a newly emerged technology in the power industry. However, its applications at full scale are still awaited. This research developed methodologies for integrated planning framework for PHES systems at grid level, employing a GIS-based model to identify feasible PHES sites, optimizing the scheduling of feasible PHES potential, and performing the financial analysis of PHES system. The methodologies were applied on grid-connected electricity area of Ontario that identified 285 feasible PHES and GPM sites with storage potential of 56,268 MWh. This research proposed the formation of a cooperative association namely „Pumped Hydro Storage Association (PHSA)‟ for integration of PHES system in the electricity market system operated by the IESO in Ontario. Using 2016 data, the optimization model resulted that PHSA supplied real-time energy 28,134 MWh/ day, provided ancillary services including variable operating reserve 23,914 MWh/ day, fixed operating reserve 4,220 MWh/ day, and purchased energy 65,060 MWh/ day. The optimization results and resultant financial indicators confirmed that proposed PHES system is technically and financially viable in a large electricity market system. As an initial step, partial development of PHES and GPM plants was proposed with an initial capital cost of C$ 1,052 Million utilizing 7,767 MWh/ day energy potential that resulted in a net profit share of C$ 13.36/ MWh for each participatory plant. Finally, the developed PHES planning framework for PHES system can certainly be found valuable to the policymakers, system operators, energy developers, research scholars, engineers, financial analysts and scientist community to work on future improvement in the PHES system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".